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matcher.py
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matcher.py
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from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
import torchaudio.transforms as T
from hifigan.models import Generator as HiFiGAN
from hifigan.utils import AttrDict
from torch import Tensor
from torchaudio.sox_effects import apply_effects_tensor
from wavlm.WavLM import WavLM
from knnvc_utils import generate_matrix_from_index
SPEAKER_INFORMATION_LAYER = 6
SPEAKER_INFORMATION_WEIGHTS = generate_matrix_from_index(SPEAKER_INFORMATION_LAYER)
def fast_cosine_dist(source_feats: Tensor, matching_pool: Tensor, device: str = 'cpu') -> Tensor:
""" Like torch.cdist, but fixed dim=-1 and for cosine distance."""
source_norms = torch.norm(source_feats, p=2, dim=-1).to(device)
matching_norms = torch.norm(matching_pool, p=2, dim=-1)
dotprod = -torch.cdist(source_feats[None].to(device), matching_pool[None], p=2)[0]**2 + source_norms[:, None]**2 + matching_norms[None]**2
dotprod /= 2
dists = 1 - ( dotprod / (source_norms[:, None] * matching_norms[None]) )
return dists
class KNeighborsVC(nn.Module):
def __init__(self,
wavlm: WavLM,
hifigan: HiFiGAN,
hifigan_cfg: AttrDict,
device='cuda'
) -> None:
""" kNN-VC matcher.
Arguments:
- `wavlm` : trained WavLM model
- `hifigan`: trained hifigan model
- `hifigan_cfg`: hifigan config to use for vocoding.
"""
super().__init__()
# set which features to extract from wavlm
self.weighting = torch.tensor(SPEAKER_INFORMATION_WEIGHTS, device=device)[:, None]
# load hifigan
self.hifigan = hifigan.eval()
self.h = hifigan_cfg
# store wavlm
self.wavlm = wavlm.eval()
self.device = torch.device(device)
self.sr = self.h.sampling_rate
self.hop_length = 320
def get_matching_set(self, wavs: list[Path] | list[Tensor], weights=None, vad_trigger_level=7) -> Tensor:
""" Get concatenated wavlm features for the matching set using all waveforms in `wavs`,
specified as either a list of paths or list of loaded waveform tensors of
shape (channels, T), assumed to be of 16kHz sample rate.
Optionally specify custom WavLM feature weighting with `weights`.
"""
feats = []
for p in wavs:
feats.append(self.get_features(p, weights=self.weighting if weights is None else weights, vad_trigger_level=vad_trigger_level))
feats = torch.concat(feats, dim=0).cpu()
return feats
@torch.inference_mode()
def vocode(self, c: Tensor) -> Tensor:
""" Vocode features with hifigan. `c` is of shape (bs, seq_len, c_dim) """
y_g_hat = self.hifigan(c)
y_g_hat = y_g_hat.squeeze(1)
return y_g_hat
@torch.inference_mode()
def get_features(self, path, weights=None, vad_trigger_level=0):
"""Returns features of `path` waveform as a tensor of shape (seq_len, dim), optionally perform VAD trimming
on start/end with `vad_trigger_level`.
"""
# load audio
if weights == None: weights = self.weighting
if type(path) in [str, Path]:
x, sr = torchaudio.load(path, normalize=True)
else:
x: Tensor = path
sr = self.sr
if x.dim() == 1: x = x[None]
if not sr == self.sr :
print(f"resample {sr} to {self.sr} in {path}")
x = torchaudio.functional.resample(x, orig_freq=sr, new_freq=self.sr)
sr = self.sr
# trim silence from front and back
if vad_trigger_level > 1e-3:
transform = T.Vad(sample_rate=sr, trigger_level=vad_trigger_level)
x_front_trim = transform(x)
# original way, disabled because it lacks windows support
#waveform_reversed, sr = apply_effects_tensor(x_front_trim, sr, [["reverse"]])
waveform_reversed = torch.flip(x_front_trim, (-1,))
waveform_reversed_front_trim = transform(waveform_reversed)
waveform_end_trim = torch.flip(waveform_reversed_front_trim, (-1,))
#waveform_end_trim, sr = apply_effects_tensor(
# waveform_reversed_front_trim, sr, [["reverse"]]
#)
x = waveform_end_trim
# extract the representation of each layer
wav_input_16khz = x.to(self.device)
if torch.allclose(weights, self.weighting):
# use fastpath
features = self.wavlm.extract_features(wav_input_16khz, output_layer=SPEAKER_INFORMATION_LAYER, ret_layer_results=False)[0]
features = features.squeeze(0)
else:
# use slower weighted
rep, layer_results = self.wavlm.extract_features(wav_input_16khz, output_layer=self.wavlm.cfg.encoder_layers, ret_layer_results=True)[0]
features = torch.cat([x.transpose(0, 1) for x, _ in layer_results], dim=0) # (n_layers, seq_len, dim)
# save full sequence
features = ( features*weights[:, None] ).sum(dim=0) # (seq_len, dim)
return features
@torch.inference_mode()
def match(self, query_seq: Tensor, matching_set: Tensor, synth_set: Tensor = None,
topk: int = 4, tgt_loudness_db: float | None = -16,
target_duration: float | None = None, device: str | None = None) -> Tensor:
""" Given `query_seq`, `matching_set`, and `synth_set` tensors of shape (N, dim), perform kNN regression matching
with k=`topk`. Inputs:
- `query_seq`: Tensor (N1, dim) of the input/source query features.
- `matching_set`: Tensor (N2, dim) of the matching set used as the 'training set' for the kNN algorithm.
- `synth_set`: optional Tensor (N2, dim) corresponding to the matching set. We use the matching set to assign each query
vector to a vector in the matching set, and then use the corresponding vector from the synth set during HiFiGAN synthesis.
By default, and for best performance, this should be identical to the matching set.
- `topk`: k in the kNN -- the number of nearest neighbors to average over.
- `tgt_loudness_db`: float db used to normalize the output volume. Set to None to disable.
- `target_duration`: if set to a float, interpolate resulting waveform duration to be equal to this value in seconds.
- `device`: if None, uses default device at initialization. Otherwise uses specified device
Returns:
- converted waveform of shape (T,)
"""
device = torch.device(device) if device is not None else self.device
if synth_set is None: synth_set = matching_set.to(device)
else: synth_set = synth_set.to(device)
matching_set = matching_set.to(device)
query_seq = query_seq.to(device)
if target_duration is not None:
target_samples = int(target_duration*self.sr)
scale_factor = (target_samples/self.hop_length) / query_seq.shape[0] # n_targ_feats / n_input_feats
query_seq = F.interpolate(query_seq.T[None], scale_factor=scale_factor, mode='linear')[0].T
dists = fast_cosine_dist(query_seq, matching_set, device=device)
best = dists.topk(k=topk, largest=False, dim=-1)
out_feats = synth_set[best.indices].mean(dim=1)
prediction = self.vocode(out_feats[None].to(device)).cpu().squeeze()
# normalization
if tgt_loudness_db is not None:
src_loudness = torchaudio.functional.loudness(prediction[None], self.h.sampling_rate)
tgt_loudness = tgt_loudness_db
pred_wav = torchaudio.functional.gain(prediction, tgt_loudness - src_loudness)
else: pred_wav = prediction
return pred_wav